Details

Autor(en) / Beteiligte

• Li, Wen-Syan
• Clifton, Chris

Titel

SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks

Ist Teil von

• Data & knowledge engineering, 2000-04, Vol.33 (1), p.49-84

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2000

Quelle

Elsevier Journal Backfiles on ScienceDirect (DFG Nationallizenzen)

Beschreibungen/Notizen

• One step in interoperating among heterogeneous databases is semantic integration: Identifying relationships between attributes or classes in different database schemas. SEMantic INTegrator (SEMINT) is a tool based on neural networks to assist in identifying attribute correspondences in heterogeneous databases. SEMINT supports access to a variety of database systems and utilizes both schema information and data contents to produce rules for matching corresponding attributes automatically. This paper provides theoretical background and implementation details of SEMINT. Experimental results from large and complex real databases are presented. We discuss the effectiveness of SEMINT and our experiences with attribute correspondence identification in various environments.